Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Sensitivity, Specificity, and Predicted Value

Sensitivity, Specificity, and Predicted Value

In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

"Missing the forest for the trees" Is there any crisis of vocation for Emergency Medicine in Italy?

Internal and emergency medicine·2026

Same author

Career Advancement Challenges for Women in Tenure Versus Clinical Tracks in Academic Medicine: Cross-Sectional Survey Study.

JMIR formative research·2026

Same author

Counseling After Obstetric Anal Sphincter Injury: How do Health Care Providers Perform?

Urogynecology (Philadelphia, Pa.)·2026

Same author

Risk Factors and Pregnancy Outcomes Associated with Insulin Addition to Metformin Therapy in Gestational Diabetes.

American journal of perinatology·2026

Same author

Improvements in Pediatric Speech Perception in Noise Using Classifier-Based Noise Management.

American journal of audiology·2026

Same author

Immediate physical therapy is beneficial for adolescent athletes with active lumbar spondylolysis: a multicentre randomised trial.

British journal of sports medicine·2025

Same journal

Regenerative Cell and Cell-Free Therapies for Necrotizing Enterocolitis: Progress Toward Clinical Translation.

American journal of perinatology·2026

Same journal

WIC Participation and the Education-Related Gap in Adverse Neonatal Outcomes: A Population-Based Cohort Study.

American journal of perinatology·2026

Same journal

Low-Dose Aspirin Prophylaxis for Preeclampsia and the Risk of Postpartum Hypertensive Outcomes: A Propensity Score Analysis.

American journal of perinatology·2026

Same journal

Cystic periventricular leukomalacia in preterm infants: Clinical risks and heart rate and oxygenation patterns.

American journal of perinatology·2026

Same journal

International consensus curriculum and competency framework for maternal point-of-care ultrasound training.

American journal of perinatology·2026

Same journal

From InSurE to MIST: A Quality Improvement Initiative in a Level IV NICU.

American journal of perinatology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Dec 11, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Predictive Models for Very Preterm Birth: Developing a Point-of-Care Tool.

Courtney L Hebert¹, Giovanni Nattino², Steven G Gabbe³

¹Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio.

American Journal of Perinatology

|August 24, 2020

Summary

This summary is machine-generated.

Researchers developed three predictive models for very preterm birth, usable at different pregnancy stages. These models demonstrated excellent calibration and can help estimate the risk of preterm birth.

More Related Videos

Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management

Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management

Published on: January 19, 2024

Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale

Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale

Published on: August 25, 2014

Related Experiment Videos

Last Updated: Dec 11, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management

Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management

Published on: January 19, 2024

Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale

Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale

Published on: August 25, 2014

Area of Science:

Maternal-fetal medicine
Predictive analytics in healthcare
Public health research

Background:

Very preterm birth (VPTB) poses significant health risks.
Accurate prediction of VPTB is crucial for timely intervention.
Existing predictive models may lack applicability at various pregnancy stages.

Purpose of the Study:

To develop and validate point-of-care predictive models for very preterm birth.
To create models utilizing data available at three distinct time points: pre-pregnancy, first-trimester end, and mid-pregnancy.
To assess the predictive performance of these models.

Main Methods:

Retrospective cohort study of 359,396 Ohio Medicaid mothers (2008-2015).
Multivariable logistic regression used to develop three predictive models.
Models validated on a separate dataset to assess performance.

Main Results:

The study included 359,396 live births, with 1.81% being very preterm births.
All developed models exhibited excellent calibration and strong goodness-of-fit.
The mid-pregnancy model showed acceptable discrimination (AUC ≈ 0.75).

Conclusions:

Point-of-care predictive models for very preterm birth were successfully developed.
These models, usable at different pregnancy stages, can estimate VPTB probability.
Further research is needed to integrate these models into interventions for VPTB prevention.